SiPSiC

1 Functions

library(stringi)
library(reticulate)
library(ggplotify)

source_from_github(repositoy = "DEG_functions",version = "0.2.47")

ℹ SHA-1 hash of file is f5bb1cd741d13bded83fe3b6fd43169e29731216
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source_from_github(repositoy = "cNMF_functions",version = "0.4.04",script_name = "cnmf_functions_V3.R")

ℹ SHA-1 hash of file is a735d9bfb5bc55fcb203f83a7240b9713fc080d3

source_from_github(repositoy = "sc_general_functions",version = "0.1.34",script_name = "functions.R")

ℹ SHA-1 hash of file is 55839fb7db3fdb3e61d8d423b0b3129bfa1d777b

2 Data

xeno <- qs::qread("./raw_data/xeno.qs")

3 DE pathways

pathwayScoresMatrix = read_rds(file = "./output_data/run_SiPSiC/pathwayScoresMatrix.RDS")
xeno[["sipsic"]] = CreateAssayObject(counts = pathwayScoresMatrix)

Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')

trace(Seurat:::FindMarkers.default,edit = T) #replace "bonferroni" with "fdr"

Warning in edit.default(def, editor = editor, file = file) :
  deparse may be incomplete
Tracing function "FindMarkers.default" in package "Seurat (not-exported)"

[1] "FindMarkers.default"

xeno = SetIdent(xeno, value = "treatment")
pathways_names = gsub(rownames(pathwayScoresMatrix), pattern = "_", replacement = "-")


logFC_df = data.frame(row.names = pathways_names)
fdr_df = data.frame(row.names = pathways_names)

for (model in unique(xeno$orig.ident)) {
  model_data = subset(xeno, subset = orig.ident == model)
  pathway_markers = FindMarkers(
    object = model_data,
    ident.1 = "OSI",
    ident.2 = "NT",
    assay = "sipsic",
    slot = "counts",
    logfc.threshold = 0,
    densify = T,
    pseudocount.use = 0.001,
    features = pathways_names
  )
  avg_log2FC = pathway_markers[pathways_names, "avg_log2FC", drop = F]
  colnames(avg_log2FC) = model
  pathway_markers  %<>%  dplyr::rename(fdr = p_val_adj)
  fdr = pathway_markers[pathways_names, "fdr", drop = F]
  colnames(fdr) = model
  
  logFC_df = cbind(logFC_df, avg_log2FC)
  fdr_df = cbind(fdr_df, fdr)
}

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fdr_df_raw = fdr_df
fdr_df = apply(fdr_df, 1:2, \(x) format(x, digits=1)) %>% as.data.frame() #round p values 
fdr_df[fdr_df == 0] = paste0("<",format(.Machine$double.xmin,digits = 1)) # replace p=0 with p<min float

untrace(Seurat:::FindMarkers.default)

Untracing function "FindMarkers.default" in package "Seurat (not-exported)"

# color row name if consistent across patients/models
colors= c()
for (row_num in 1:nrow(logFC_df)) {
  row = logFC_df[row_num,]
  if (sum(row > 0.15)  >= 4) { # if at least x values are more than 0.1 log2FC
    colors <- c(colors, "red")
  }
  else if (sum(row < -0.15)  >= 4) {
    colors <- c(colors, "blue")
  }
  else{colors <- c(colors, "black")}
}

col_fun = circlize::colorRamp2(c(-1, 0, 1), c("blue", "white", "red"))
p = ComplexHeatmap::Heatmap(logFC_df, name = "log2FC on/pre", col = col_fun,
    cell_fun = function(j, i, x, y, width, height, fill) {
        grid.text(fdr_df[i, j], x, y, gp = gpar(fontsize = 8))
}, row_names_gp = gpar(fontsize = 8,col =colors), clustering_method_rows = "average", clustering_distance_rows = "euclidean")

Warning: The input is a data frame, convert it to a matrix.

p

Warning in grSoftVersion() :
  unable to load shared object '/usr/local/lib/R/modules//R_X11.so':
  libXt.so.6: cannot open shared object file: No such file or directory

```r
saveRDS(object = as.grob(p),file = \./Figures/xeno_sipsic_heatmap.rds\)

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# Heatmap with asterisks




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```r
# Description
fdr_df_sig = fdr_df_raw
fdr_df_sig[] <- lapply(fdr_df_sig, as.numeric)

convert <- function(vec) {
  symnum(vec, corr = FALSE, na = FALSE, cutpoints = c(0, 
    0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", "**", "*", ".", " ")) %>% as.character()
} 

fdr_df_sig[] = apply(fdr_df_sig, MARGIN = 1, FUN = convert)

col_fun = circlize::colorRamp2(c(-1, 0, 1), c("blue", "white", "red"))
p = ComplexHeatmap::Heatmap(logFC_df, name = "log2FC on/pre", col = col_fun,
    cell_fun = function(j, i, x, y, width, height, fill) {
        grid.text(fdr_df_sig[i, j], x, y, gp = gpar(fontsize = 8))
}, row_names_gp = gpar(fontsize = 8,col =colors), clustering_method_rows = "average", clustering_distance_rows = "euclidean")

Warning: The input is a data frame, convert it to a matrix.

p

4 Heatmap

DefaultAssay(xeno) = "sipsic"
data = FetchData(object = xeno, vars = c(pathways_names,"orig.ident"))
data[] <- data %>%  group_by(orig.ident) %>% mutate(across(is.numeric, ~ as.numeric(scale(.)))) %>% ungroup() 
data = data %>% select(-orig.ident) 
data = data %>% t() %>% as.data.frame()

annotation_data = FetchData(object = xeno, vars = c("orig.ident","treatment")) %>% dplyr::arrange(orig.ident,treatment)
column_ha = HeatmapAnnotation(df = annotation_data[,2])
data = data[,rownames(annotation_data)] #order data like annotation
data = na.omit(data)
p = ComplexHeatmap::Heatmap(
  data,
  show_column_names = F,
  row_names_gp = grid::gpar(fontsize = 7),
  cluster_rows = T,
  top_annotation = column_ha,
  name = "Z-score expression",use_raster = F,cluster_columns = F,column_split   = annotation_data$orig.ident,
  column_gap = unit(2, "mm"), border = TRUE,show_parent_dend_line = FALSE, show_column_dend = FALSE,cluster_column_slices = F
)

Warning: The input is a data frame, convert it to a matrix.
The automatically generated colors map from the minus and plus 99^th of the absolute values in the
matrix. There are outliers in the matrix whose patterns might be hidden by this color mapping. You can
manually set the color to `col` argument.

Use `suppressMessages()` to turn off this message.

  
p

# heatmap with clutering per model and time point
DefaultAssay(xeno) = "sipsic"
data = FetchData(object = xeno, vars = c(pathways_names,"orig.ident"))
data[] <- data %>%  group_by(orig.ident) %>%
  mutate(across(is.numeric, ~ as.numeric(scale(.)))) %>% ungroup() # scale per model

Warning: Problem while computing `..1 = across(is.numeric, ~as.numeric(scale(.)))`.
ℹ Predicate functions must be wrapped in `where()`.

# Bad data %>% select(is.numeric)

# Good data %>% select(where(is.numeric))

ℹ Please update your code. This message is displayed once per session.
ℹ The warning occurred in group 0: character(0).

data = data %>% select(-orig.ident) # remove ident var
data = data %>% t() %>% as.data.frame()

annotation_data = FetchData(object = xeno, vars = c("orig.ident","treatment")) %>% dplyr::arrange(orig.ident,treatment)
column_ha = HeatmapAnnotation(Treatment = annotation_data[,2], #set annotation
                              col = list(Treatment = c("NT" = "red", "OSI" = "green", "res" = "blue"))) 
data = data[,rownames(annotation_data)] #order data like annotation
data = na.omit(data)
column_split = paste(annotation_data$orig.ident,annotation_data$treatment,sep = "\n") #split by ident+tp
column_names = unique(column_split) %>% gsub(pattern = "119\nOSI",replacement = "      119\n      OSI") #adujst location to avoid overlap
DefaultAssay(xeno) = "RNA" #make RNA default again

p = ComplexHeatmap::Heatmap(
  data,
  show_column_names = F,
  row_names_gp = grid::gpar(fontsize = 10),
  cluster_rows = T,
  top_annotation = column_ha,
  name = "Z-score expression",use_raster = F,cluster_columns = T,column_split = column_split,
  column_title = column_names,
  column_gap = unit(2, "mm"), border = TRUE,show_parent_dend_line = FALSE, show_column_dend = F,cluster_column_slices = F)

Warning: The input is a data frame, convert it to a matrix.
The automatically generated colors map from the minus and plus 99^th of the absolute values in the matrix. There are outliers
in the matrix whose patterns might be hidden by this color mapping. You can manually set the color to `col` argument.

Use `suppressMessages()` to turn off this message.

  
p

# only nmf programs
for (patient_name in unique(xeno$orig.ident)) {
  
  patient_data = subset(xeno,subset = orig.ident == patient_name)
  pathways_names = c("HALLMARK-INTERFERON-ALPHA-RESPONSE","HALLMARK-TNFA-SIGNALING-VIA-NFKB", "HALLMARK-HYPOXIA","HALLMARK-E2F-TARGETS" )
  
  DefaultAssay(patient_data) = "sipsic"
  data = FetchData(object = patient_data, vars = c(pathways_names))
  data[] <- data %>% 
    mutate(across(everything(), ~ as.numeric(scale(.))))  # scale per pathway
  data = data %>% t() %>% as.data.frame()
  
  annotation_data = FetchData(object = patient_data, vars = c("treatment")) %>% dplyr::arrange(treatment)
  column_ha = HeatmapAnnotation(Treatment = annotation_data[,1], #set annotation
                                col = list(Treatment = c("NT" = "red", "OSI" = "green", "res" = "blue"))) 
  data = data[,rownames(annotation_data)] #order data like annotation
  data = na.omit(data)
  DefaultAssay(xeno) = "RNA" #make RNA default again
  
  p = ComplexHeatmap::Heatmap(
    data,
    show_column_names = F,
    row_names_gp = grid::gpar(fontsize = 10),
    cluster_rows = T,
    top_annotation = column_ha,
    name = "Z-score expression",use_raster = F,cluster_columns = T,column_split = annotation_data$treatment,
    column_gap = unit(2, "mm"), border = TRUE,show_parent_dend_line = FALSE, show_column_dend = T,cluster_column_slices = F,column_title = patient_name)
  
    
  print(p)
}

Warning: The input is a data frame, convert it to a matrix.
Warning in grSoftVersion() :
  unable to load shared object '/usr/local/lib/R/modules//R_X11.so':
  libXt.so.6: cannot open shared object file: No such file or directory
Warning: The input is a data frame, convert it to a matrix.
The automatically generated colors map from the minus and plus 99^th of the absolute values in the matrix. There are outliers
in the matrix whose patterns might be hidden by this color mapping. You can manually set the color to `col` argument.

Use `suppressMessages()` to turn off this message.
Warning: The input is a data frame, convert it to a matrix.
The automatically generated colors map from the minus and plus 99^th of the absolute values in the matrix. There are outliers
in the matrix whose patterns might be hidden by this color mapping. You can manually set the color to `col` argument.

Use `suppressMessages()` to turn off this message.
Warning: The input is a data frame, convert it to a matrix.
Warning: The input is a data frame, convert it to a matrix.
The automatically generated colors map from the minus and plus 99^th of the absolute values in the matrix. There are outliers
in the matrix whose patterns might be hidden by this color mapping. You can manually set the color to `col` argument.

Use `suppressMessages()` to turn off this message.
Warning: The input is a data frame, convert it to a matrix.
The automatically generated colors map from the minus and plus 99^th of the absolute values in the matrix. There are outliers
in the matrix whose patterns might be hidden by this color mapping. You can manually set the color to `col` argument.

Use `suppressMessages()` to turn off this message.

5 Correlation

# Description
for (patient_name in unique(xeno$orig.ident)) {
  
  patient_data = subset(xeno, subset = orig.ident == patient_name)
  pathways_names = c(
    "HALLMARK-INTERFERON-ALPHA-RESPONSE",
    "HALLMARK-TNFA-SIGNALING-VIA-NFKB",
    "HALLMARK-HYPOXIA",
    "HALLMARK-E2F-TARGETS"
  )
  
  DefaultAssay(patient_data) = "sipsic"
  data = FetchData(object = patient_data, vars = c(pathways_names))
  data[] <- data %>%
    mutate(across(everything(), ~ as.numeric(scale(.))))  # scale per pathway
  
  
  data = na.omit(data)
  DefaultAssay(xeno) = "RNA" #make RNA default again
  colnames(data) = c("H_INFa", "H_TNFa_NFKb", "H_HYPOXIA", "H_E2F")
  library(circlize)
  data = cor(data)
  col_fun = colorRamp2(c(-1, 0, 1), c("blue", "white", "red"))
 p =  Heatmap(
    data, name = "pearson", col = col_fun, show_row_names = F, column_title = patient_name,
    cell_fun = function(j, i, x, y, width, height, fill) {
      grid.text(as.character(round(data[i, j], digits = 2)), x, y, gp = gpar(fontsize = 10))
    }
  )
 print(p)
}

6 Ternary plot

p_list

$`119`

$PC9

$`1109`

$`1071`

$`1157`

$`1068`

p_list

$`119`

Warning: Removed 263 rows containing missing values (geom_point).

$PC9

Warning: Removed 4629 rows containing missing values (geom_point).

$`1109`

Warning: Removed 2574 rows containing missing values (geom_point).

$`1071`

Warning: Removed 2918 rows containing missing values (geom_point).

$`1157`
Error in `f()`:
! Insufficient values in manual scale. 3 needed but only 2 provided.
Backtrace:
  1. base `<fn>`(x)
  2. ggtern:::print.ggplot(x)
  4. ggtern:::ggplot_build.ggplot(x)
  5. base::lapply(data, ggint$scales_map_df, scales = npscales)
  6. ggplot2 FUN(X[[i]], ...)
     ...
 13. ggplot2 FUN(X[[i]], ...)
 14. self$map(df[[j]])
 15. ggplot2 f(..., self = self)
 16. self$palette(n)
 17. ggplot2 f(...)

 ggtern(data=df,ggtern::aes(x,y,z)) + geom_point()+
  theme_tropical(base_size=14)

[1] "not scaling"
[1] "not scaling"
[1] "not scaling"
[1] "not scaling"
[1] "not scaling"
[1] "not scaling"
[1] "not scaling"
[1] "not scaling"
[1] "not scaling"
[1] "not scaling"
[1] "not scaling"
[1] "not scaling"

# plot
do.call(ggtern::grid.arrange,args = c(p_list,ncol=3))